Build your AI search strategy on data you own, not on a visibility tool

A review of 194 tools launched over the past two years to help brands improve their AI visibility found that just over 60% of them are lightweight experiments, side projects and products built by solo founders. The failure rate for ventures like these runs between 55% and 60%. The takeaway: no company should let a piece of software dictate its AI search strategy.

The analysis appeared in a column published by Search Engine Journal on November 12. The tools were sourced from Product Hunt, G2 and LinkedIn, so the count leaves out any that have no presence on those platforms. According to the column, every month several of them shut down their APIs, let their domains lapse or pivot to apps that have nothing to do with the space.

The main reason is cost. Querying a search engine and parsing the HTML costs a fraction of what it takes to fire off chains of prompts with live browsing. Inference calls, headless browsers, proxies to get around blocks and context tokens all add up, and Google actively works to fight this kind of scraping.

For a tool that charges $100 a month and runs weekly prompt variations across a client's catalog, the margin simply isn't there, the column says. All it takes is for the model provider to change inference pricing, tighten usage limits or alter the interface, and the business breaks.

Switching tools means switching metrics

There is no standard way to measure AI visibility. Every tool builds its own index and sells it as a differentiator. That means moving from one platform to another is a switch in measurement philosophy, with no equivalence between the numbers.

Then there is the natural variability of the answers. The sources AI models cite shift a lot from one query to the next, without any change to the brand's site or its competitors' sites. Treating this like a rank tracker sends teams chasing drops and gains that come purely from model variance.

What brands should keep in house

The recommendation is to keep the set of queries that matter inside the company: prompts organized by customer intent and by category, high-value comparison queries and the questions that define the category. This material should live in the company's own documentation, independent of any subscription.

When choosing a vendor, the column suggests favoring platforms that export raw data via webhook, a BigQuery connector or a standardized CSV, including the answer text, date and time, source URLs and the prompt used. That way, if the vendor changes how it calculates its scores, the historical record stays intact.

For brand leaders, the message is to treat visibility as an outcome. A tracking dashboard doesn't build authority. In the column's view, models cite brands that have clear, consistent definitions of who they are, unambiguous structure and structured data, and an active digital presence.